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It is governed by a custom-engineered `LLMFailoverRouter` that prioritizes extreme execution speed while guaranteeing high availability.\n\n```mermaid\nflowchart TD\n    subgraph DataLayer [\"Data Layer\"]\n        DB[(\"DuckDB\\nSales Data\")]\n    end\n\n    subgraph FailoverRouter [\"LLM Failover Router\"]\n        Router{\"API Gateway\"}\n        Groq[\"Groq Llama 3\\nPrimary - Fast\"]\n        Gem[\"Google Gemini\\nFallback - Smart\"]\n        Router -->|\"Success\"| Groq\n        Router -->|\"Rate Limited / 429\"| Gem\n    end\n\n    subgraph CrewAI [\"CrewAI Orchestration\"]\n        A1[\"Data Analyst Agent\"]\n        A2[\"Market Researcher Agent\"]\n        A3[\"Reporting Analyst Agent\"]\n        \n        DB -->|\"SQL Tools\"| A1\n        A1 -->|\"Chart + Summary\"| A2\n        A2 -->|\"Web Search Tools\"| A3\n    end\n\n    Router -.->|\"Powers\"| A1\n    Router -.->|\"Powers\"| A2\n    Router -.->|\"Powers\"| A3\n    A3 -->|\"Compiles\"| Output([\"Multimodal PDF Report\"])\n\n    style Router fill:#f9a826,stroke:#333,stroke-width:2px,color:#000\n    style Output fill:#FF4B4B,stroke:#333,stroke-width:2px,color:#fff\n```\n---\n\n## ⚙️ Key Engineering Features\n\n* **Robust LLM Failover Strategy:** The core of the pipeline's reliability. A custom `LLMFailoverRouter` assigns a high-speed Groq model (`llama-3.1-8b-instant`) to all agents for a sub-60-second runtime. If the Groq API hits a rate limit (HTTP 429), the router automatically and silently fails over to a backup model (`gemini-1.5-flash`) without crashing the execution state.\n* **Autonomous Agent Crew:** Employs specialized AI personas (Data Analyst, Market Researcher, Reporting Analyst) built on CrewAI to collaborate, delegate, and achieve complex analytical goals sequentially.\n* **Custom Tool Engineering:** Agents operate autonomously using a suite of custom-built tools for localized SQL execution (`DuckDB`), file I/O operations, programmatic data visualization (`Seaborn`/`Matplotlib`), and real-time web crawling (`Tavily`).\n* **Multimodal Delivery:** The final pipeline deliverable is an automated PDF report that programmatically embeds generated data visualization charts (`.png`) alongside the synthesized executive text and full agent execution logs.\n\n---\n\n## 💻 Technology Stack\n\n* **Agentic Framework:** CrewAI, LangChain\n* **LLM Providers:** Groq (Primary), Google Gemini (Failover)\n* **Data Backend:** DuckDB, Pandas\n* **Visualization:** Matplotlib, Seaborn\n* **Infrastructure & Tools:** Tavily API (Search), FPDF2 (Reporting)\n\n---\n\n## 🚀 Reproducibility & Setup\n\n### 1. Repository Setup\nClone the repository and install the required dependencies (Python 3.10+ recommended):\n\n```bash\ngit clone [https://github.com/AD1007/Multimodal-Agentic-Business-Intelligence-Analyst.git](https://github.com/AD1007/Multimodal-Agentic-Business-Intelligence-Analyst.git)\ncd Multimodal-Agentic-Business-Intelligence-Analyst\n\npip install -r requirements.txt\n```\n\n### 2. Environment Configuration\nCreate a `.env` file in the root directory to authenticate the LLM router and search tools:\n\n```env\nGROQ_API_KEY=\"your_groq_key_here\"\nGOOGLE_API_KEY=\"your_gemini_key_here\"\nTAVILY_API_KEY=\"your_tavily_key_here\"\n```\n\n### 3. Pipeline Execution\nExecute the master orchestration script. 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